Elastic announced enhancements across the Elastic Search Platform and its solutions.
The general availability of dozens of prebuilt Elastic Agent data integrations extends visibility into complex and distributed cloud-native services, enabling users to more quickly solve their data challenges, improve operational efficiency, and, ultimately, provide a better customer experience. New capabilities also include observability tooling for continuous integration and continuous delivery (CI/CD) pipelines with integrations with Ansible, now generally available.
Deepening its existing integrations through new extension points for Elastic cases and alerts, Elastic now offers customers two certified applications in the ServiceNow Store that provide enhanced control over both IT Service Management (ITSM) and Security Incident Response (SIR) integrations and the data that they transmit.
Elastic also introduced a new ServiceNow IT Operations Management (ITOM) integration to streamline workflows for proactive IT operations. By integrating Elastic with ServiceNow, organizations can standardize and streamline alerting and case management workflows.
Elastic also introduced an AWS FireLens integration, allowing customers to directly ship container logs and events to Elastic Cloud. AWS FireLens is a container log router for Amazon Elastic Container Service (Amazon ECS) launch types, Amazon Elastic Compute Cloud (Amazon EC2) and AWS Fargate.
Additionally, the beta availability of automated curations enables Elastic App Search customers to harness the power of collected analytics and automated suggestions to create better search experiences.
Other key updates across the Elastic Stack, Elastic Cloud and solutions include:
- Elastic Stack and Elastic Cloud: Adding integrations with AWS Web Application Firewall (WAF), Cisco Duo, GitHub, Crowdstrike, and 1Password to the dozens of generally available pre-built integrations for Elastic Agent, helping customers simplify data collection and normalization. Additionally, a unified management interface in Kibana simplifies the management of distributed endpoint agents, whether customers are using Elastic Agent, Logstash, Beats, or use case-specific data integrations like the App Search Web Crawler.
- Elastic Enterprise Search: Enhanced capabilities in Elastic App Search include support for Google Firebase, enabling users to build premium search experiences into their applications by seamlessly indexing their data to Elastic Cloud. App Search and Workplace Search features are also now accessible in Kibana from a single management interface, providing users with a unified search experience when monitoring and visualizing their search data.
- Elastic Observability: Elastic introduces curated data exploration views to provide users with the ability to visualize and overlay multiple dimensions of data. Curated data exploration views are generally available for real user monitoring and synthetics, and in technical preview for mobile APM. Additionally, customers can now start leveraging the centralized management, scalability, security, and one-click integrations of the unified Elastic Agent across web, datastore, middleware, edge, and cloud-native infrastructure.
“As business leaders accelerate their digital transformation initiatives, they increasingly look to their data to provide actionable insights,” said Ash Kulkarni, Chief Product Officer, Elastic. “With the leading platform for search-powered solutions, Elastic helps organizations, their employees, and their customers drive the results that matter—helping people find what they need faster, keeping mission-critical applications running smoothly, and protecting against cyber threats.”
The Latest
I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...
Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...
For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...
Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...
Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...
For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...
New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...
Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...
In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ...
In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...